Using the Saccharomyces Genome Database (SGD) for analysis of protein similarities and structure

TREX tetramer disruption alters RNA processing necessary for corticogenesis in THOC6 Intellectual Disability Syndrome

THOC6 variants are the genetic basis of autosomal recessive THOC6 Intellectual Disability Syndrome (TIDS). THOC6 is critical for mammalian Transcription Export complex (TREX) tetramer formation, which is composed of four six-subunit THO monomers. The TREX tetramer facilitates mammalian RNA processing, in addition to the nuclear mRNA export functions of the TREX dimer conserved through yeast. Human and mouse TIDS model systems revealed novel THOC6-dependent, species-specific TREX tetramer functions. Germline biallelic Thoc6 loss-of-function (LOF) variants result in mouse embryonic lethality. Biallelic THOC6 LOF variants reduce the binding affinity of ALYREF to THOC5 without affecting the protein expression of TREX members, implicating impaired TREX tetramer formation. Defects in RNA nuclear export functions were not detected in biallelic THOC6 LOF human neural cells. Instead, mis-splicing was detected in human and mouse neural tissue, revealing novel THOC6-mediated TREX coordination of mRNA processing. We demonstrate that THOC6 is required for key signaling pathways known to regulate the transition from proliferative to neurogenic divisions during human corticogenesis. Together, these findings implicate altered RNA processing in the developmental biology of TIDS neuropathology.

Mechanisms of mRNA processing defects in inherited THOC6 intellectual disability syndrome

THOC6 is the genetic basis of autosomal recessive THOC6 Intellectual Disability Syndrome (TIDS). THOC6 facilitates the formation of the Transcription Export complex (TREX) tetramer, composed of four THO monomers. The TREX tetramer supports mammalian mRNA processing that is distinct from yeast TREX dimer functions. Human and mouse TIDS model systems allow novel THOC6-dependent TREX tetramer functions to be investigated. Biallelic loss-of-functon(LOF) THOC6 variants do not influence the expression and localization of TREX members in human cells, but our data suggests reduced binding affinity of ALYREF. Impairment of TREX nuclear export functions were not detected in cells with biallelic THOC6 LOF. Instead, mRNA mis-splicing was observed in human and mouse neural tissue, revealing novel insights into THOC6-mediated TREX coordination of mRNA processing. We demonstrate that THOC6 is required for regulation of key signaling pathways in human corticogenesis that dictate the transition from proliferative to neurogenic divisions that may inform TIDS neuropathology.

Saccharomyces cerevisiae NRE1 and IRC24 Encode Paralogous Benzil Oxidoreductases

Irc24p is a benzil oxidoreductase encoded on chromosome IX of Saccharomyces cerevisiae . We identified a putative paralog, Nre1p, encoded 284 bp downstream. Both proteins are small, cytoplasmic, and are 52% identical (70% similar). PANTHER and PFAM analysis of the amino acid sequences and rigid pairwise structure alignment predicted a conserved active site and Rossmann folds in both, implicating NADH or NADPH as likely cofactors. We purified hexahistidine-tagged Irc24p and Nre1p. Both proteins catalyze the reduction of the diketone benzil with similar kinetics and a preference for NADPH. This is the first demonstration of in vitro function for Nre1p.

Modelling the fitness landscapes of a SCRaMbLEd yeast genome

The use of microorganisms for the production of industrially important compounds and enzymes is becoming increasingly important. Eukaryotes have been less widely used than prokaryotes in biotechnology, because of the complexity of their genomic structure and biology. The Yeast2.0 project is an international effort to engineer the yeast Saccharomyces cerevisiae to make it easy to manipulate, and to generate random variants using a system called SCRaMbLE. SCRaMbLE relies on artificial evolution in vitro to identify useful variants, an approach which is time consuming and expensive. We developed an in silico simulator for the SCRaMbLE system, using an evolutionary computing approach, which can be used to investigate and optimize the fitness landscape of the system. We applied the system to the investigation of the fitness landscape of one of the S. saccharomyces chromosomes, and found that our results fitted well with those previously published. We then simulated directed evolution with or without manipulation of SCRaMbLE, and revealed that controlling the SCRaMbLE process could effectively impact directed evolution. Our simulator can be applied to the analysis of the fitness landscapes of any organism for which SCRaMbLE has been implemented.

New data and collaborations at the Saccharomyces Genome Database: updated reference genome, alleles, and the Alliance of Genome Resources

Saccharomyces cerevisiae is used to provide fundamental understanding of eukaryotic genetics, gene product function, and cellular biological processes. Saccharomyces Genome Database (SGD) has been supporting the yeast research community since 1993, serving as its de facto hub. Over the years, SGD has maintained the genetic nomenclature, chromosome maps, and functional annotation, and developed various tools and methods for analysis and curation of a variety of emerging data types. More recently, SGD and six other model organism focused knowledgebases have come together to create the Alliance of Genome Resources to develop sustainable genome information resources that promote and support the use of various model organisms to understand the genetic and genomic bases of human biology and disease. Here we describe recent activities at SGD, including the latest reference genome annotation update, the development of a curation system for mutant alleles, and new pages addressing homology across model organisms as well as the use of yeast to study human disease.

A functionally divergent intrinsically disordered region underlying the conservation of stochastic signaling

Stochastic signaling dynamics expand living cells' information processing capabilities. An increasing number of studies report that regulators encode information in their pulsatile dynamics. The evolutionary mechanisms that lead to complex signaling dynamics remain uncharacterized, perhaps because key interactions of signaling proteins are encoded in intrinsically disordered regions (IDRs), whose evolution is difficult to analyze. Here we focused on the IDR that controls the stochastic pulsing dynamics of Crz1, a transcription factor in fungi downstream of the widely conserved calcium signaling pathway. We find that Crz1 IDRs from anciently diverged fungi can all respond transiently to calcium stress; however, only Crz1 IDRs from the Saccharomyces clade support pulsatility, encode extra information, and rescue fitness in competition assays, while the Crz1 IDRs from distantly related fungi do none of the three. On the other hand, we find that Crz1 pulsing is conserved in the distantly related fungi, consistent with the evolutionary model of stabilizing selection on the signaling phenotype. Further, we show that a calcineurin docking site in a specific part of the IDRs appears to be sufficient for pulsing and show evidence for a beneficial increase in the relative calcineurin affinity of this docking site. We propose that evolutionary flexibility of functionally divergent IDRs underlies the conservation of stochastic signaling by stabilizing selection.

GEsture: an online hand-drawing tool for gene expression pattern search

Gene expression profiling data provide useful information for the investigation of biological function and process. However, identifying a specific expression pattern from extensive time series gene expression data is not an easy task. Clustering, a popular method, is often used to classify similar expression genes, however, genes with a 'desirable' or 'user-defined' pattern cannot be efficiently detected by clustering methods. To address these limitations, we developed an online tool called GEsture. Users can draw, or graph a curve using a mouse instead of inputting abstract parameters of clustering methods. GEsture explores genes showing similar, opposite and time-delay expression patterns with a gene expression curve as input from time series datasets. We presented three examples that illustrate the capacity of GEsture in gene hunting while following users' requirements. GEsture also provides visualization tools (such as expression pattern figure, heat map and correlation network) to display the searching results. The result outputs may provide useful information for researchers to understand the targets, function and biological processes of the involved genes.

Polyene antibiotic that inhibits membrane transport proteins

The limited therapeutic arsenal and the increase in reports of fungal resistance to multiple antifungal agents have made fungal infections a major therapeutic challenge. The polyene antibiotics are the only group of antifungal antibiotics that directly target the plasma membrane via a specific interaction with the main fungal sterol, ergosterol, often resulting in membrane permeabilization. In contrast to other polyene antibiotics that form pores in the membrane, the mode of action of natamycin has remained obscure but is not related to membrane permeabilization. Here, we demonstrate that natamycin inhibits growth of yeasts and fungi via the immediate inhibition of amino acid and glucose transport across the plasma membrane. This is attributable to ergosterol-specific and reversible inhibition of membrane transport proteins. It is proposed that ergosterol-dependent inhibition of membrane proteins is a general mode of action of all the polyene antibiotics, of which some have been shown additionally to permeabilize the plasma membrane. Our results imply that sterol-protein interactions are fundamentally important for protein function even for those proteins that are not known to reside in sterol-rich domains.

Deletion of many yeast introns reveals a minority of genes that require splicing for function

Splicing regulates gene expression and contributes to proteomic diversity in higher eukaryotes. However, in yeast only 283 of the 6000 genes contain introns and their impact on cell function is not clear. To assess the contribution of introns to cell function, we initiated large-scale intron deletions in yeast with the ultimate goal of creating an intron-free model eukaryote. We show that about one-third of yeast introns are not essential for growth. Only three intron deletions caused severe growth defects, but normal growth was restored in all cases by expressing the intronless mRNA from a heterologous promoter. Twenty percent of the intron deletions caused minor phenotypes under different growth conditions. Strikingly, the combined deletion of all introns from the 15 cytoskeleton-related genes did not affect growth or strain fitness. Together, our results show that although the presence of introns may optimize gene expression and provide benefit under stress, a majority of introns could be removed with minor consequences on growth under laboratory conditions, supporting the view that many introns could be phased out of Saccharomyces cerevisiae without blocking cell growth.

Mimicking Cellular Sorting Improves Prediction of Subcellular Localization

Predicting the native subcellular compartment of a protein is an important step toward elucidating its function. Here we introduce LOCtree, a hierarchical system combining support vector machines (SVMs) and other prediction methods. LOCtree predicts the subcellular compartment of a protein by mimicking the mechanism of cellular sorting and exploiting a variety of sequence and predicted structural features in its input. Currently LOCtree does not predict localization for membrane proteins, since the compositional properties of membrane proteins significantly differ from those of non-membrane proteins. While any information about function can be used by the system, we present estimates of performance that are valid when only the amino acid sequence of a protein is known. When evaluated on a non-redundant test set, LOCtree achieved sustained levels of 74% accuracy for non-plant eukaryotes, 70% for plants, and 84% for prokaryotes. We rigorously benchmarked LOCtree in comparison to the best alternative methods for localization prediction. LOCtree outperformed all other methods in nearly all benchmarks. Localization assignments using LOCtree agreed quite well with data from recent large-scale experiments. Our preliminary analysis of a few entirely sequenced organisms, namely human (Homo sapiens), yeast (Saccharomyces cerevisiae), and weed (Arabidopsis thaliana) suggested that over 35% of all non-membrane proteins are nuclear, about 20% are retained in the cytosol, and that every fifth protein in the weed resides in the chloroplast.

Protein-protein Interaction Networks: from Interactions to Networks

The goal of interaction proteomics that studies the protein-protein interactions of all expressed proteins is to understand biological processes that are strictly regulated by these interactions. The availability of entire genome sequences of many organisms and high-throughput analysis tools has led scientists to study the entire proteome (Pandey and Mann, 2000). There are various high-throughput methods for detecting protein interactions such as yeast two-hybrid approach and mass spectrometry to produce vast amounts of data that can be utilized to decipher protein functions in complicated biological networks. In this review, we discuss recent developments in analytical methods for large-scale protein interactions and the future direction of interaction proteomics.

A method to assess compositional bias in biological sequences and its application to prion-like glutamine/asparagine-rich domains in eukaryotic proteomes

We have derived a novel method to assess compositional biases in biological sequences, which is based on finding the lowest-probability subsequences for a given residue-type set. As a case study, the distribution of prion-like glutamine/asparagine-rich ((Q+N)-rich) domains (which are linked to amyloidogenesis) was assessed for budding and fission yeasts and four other eukaryotes. We find more than 170 prion-like (Q+N)-rich regions in budding yeast, and, strikingly, many fewer in fission yeast. Also, some residues, such as tryptophan or isoleucine, are unlikely to form biased regions in any eukaryotic proteome.

An automated method for finding molecular complexes in large protein interaction networks

BACKGROUND

Recent advances in proteomics technologies such as two-hybrid, phage display and mass spectrometry have enabled us to create a detailed map of biomolecular interaction networks. Initial mapping efforts have already produced a wealth of data. As the size of the interaction set increases, databases and computational methods will be required to store, visualize and analyze the information in order to effectively aid in knowledge discovery.

RESULTS

This paper describes a novel graph theoretic clustering algorithm, "Molecular Complex Detection" (MCODE), that detects densely connected regions in large protein-protein interaction networks that may represent molecular complexes. The method is based on vertex weighting by local neighborhood density and outward traversal from a locally dense seed protein to isolate the dense regions according to given parameters. The algorithm has the advantage over other graph clustering methods of having a directed mode that allows fine-tuning of clusters of interest without considering the rest of the network and allows examination of cluster interconnectivity, which is relevant for protein networks. Protein interaction and complex information from the yeast Saccharomyces cerevisiae was used for evaluation.

CONCLUSION

Dense regions of protein interaction networks can be found, based solely on connectivity data, many of which correspond to known protein complexes. The algorithm is not affected by a known high rate of false positives in data from high-throughput interaction techniques. The program is available from ftp://ftp.mshri.on.ca/pub/BIND/Tools/MCODE.

An automated method for finding molecular complexes in large protein interaction networks

BACKGROUND

Recent advances in proteomics technologies such as two-hybrid, phage display and mass spectrometry have enabled us to create a detailed map of biomolecular interaction networks. Initial mapping efforts have already produced a wealth of data. As the size of the interaction set increases, databases and computational methods will be required to store, visualize and analyze the information in order to effectively aid in knowledge discovery.

RESULTS

This paper describes a novel graph theoretic clustering algorithm, "Molecular Complex Detection" (MCODE), that detects densely connected regions in large protein-protein interaction networks that may represent molecular complexes. The method is based on vertex weighting by local neighborhood density and outward traversal from a locally dense seed protein to isolate the dense regions according to given parameters. The algorithm has the advantage over other graph clustering methods of having a directed mode that allows fine-tuning of clusters of interest without considering the rest of the network and allows examination of cluster interconnectivity, which is relevant for protein networks. Protein interaction and complex information from the yeast Saccharomyces cerevisiae was used for evaluation.

CONCLUSION

Dense regions of protein interaction networks can be found, based solely on connectivity data, many of which correspond to known protein complexes. The algorithm is not affected by a known high rate of false positives in data from high-throughput interaction techniques. The program is available from ftp://ftp.mshri.on.ca/pub/BIND/Tools/MCODE.

A method to assess compositional bias in biological sequences and its application to prion-like glutamine/asparagine-rich domains in eukaryotic proteomes

We have derived a novel method to assess compositional biases in biological sequences, which is based on finding the lowest-probability subsequences for a given residue-type set. As a case study, the distribution of prion-like glutamine/asparagine-rich ((Q+N)-rich) domains (which are linked to amyloidogenesis) was assessed for budding and fission yeasts and four other eukaryotes. We find more than 170 prion-like (Q+N)-rich regions in budding yeast, and, strikingly, many fewer in fission yeast. Also, some residues, such as tryptophan or isoleucine, are unlikely to form biased regions in any eukaryotic proteome.

Analyzing yeast protein-protein interaction data obtained from different sources

High-throughput methods for detecting protein interactions, such as mass spectrometry and yeast two-hybrid assays, continue to produce vast amounts of data that may be exploited to infer protein function and regulation. As this article went to press, the pool of all published interaction information on Saccharomyces cerevisiae was 15,143 interactions among 4,825 proteins, and power-law scaling supports an estimate of 20,000 specific protein interactions. To investigate the biases, overlaps, and complementarities among these data, we have carried out an analysis of two high-throughput mass spectrometry (HMS)-based protein interaction data sets from budding yeast, comparing them to each other and to other interaction data sets. Our analysis reveals 198 interactions among 222 proteins common to both data sets, many of which reflect large multiprotein complexes. It also indicates that a "spoke" model that directly pairs bait proteins with associated proteins is roughly threefold more accurate than a "matrix" model that connects all proteins. In addition, we identify a large, previously unsuspected nucleolar complex of 148 proteins, including 39 proteins of unknown function. Our results indicate that existing large-scale protein interaction data sets are nonsaturating and that integrating many different experimental data sets yields a clearer biological view than any single method alone.

Probabilistic prediction of Saccharomyces cerevisiae mRNA 3'-processing sites

We present a tool for the prediction of mRNA 3'-processing (cleavage and polyadenylation) sites in the yeast Saccharomyces cerevisiae, based on a discrete state-space model or hidden Markov model. Comparison of predicted sites with experimentally verified 3'-processing sites indicates good agreement. All predicted or known yeast genes were analyzed to find probable 3'-processing sites. Known alternative 3'-processing sites, both within the 3'-untranslated region and within the protein coding sequence were successfully identified, leading to the possibility of prediction of previously unknown alternative sites. The lack of an apparent 3'-processing site calls into question the validity of some predicted genes. This is specifically investigated for predicted genes with overlapping coding sequences.

Applications of yeast in drug discovery

The yeast Saccharomyces cerevisiae is perhaps the best-studied eukaryotic organism. Its experimental tractability, combined with the remarkable conservation of gene function throughout evolution, makes yeast the ideal model genetic organism. Yeast is a non-pathogenic model of fungal pathogens used to identify antifungal targets suitable for drug development and to elucidate mechanisms of action of antifungal agents. As a model of fundamental cellular processes and metabolic pathways of the human, yeast has improved our understanding and facilitated the molecular analysis of many disease genes. The completion of the Saccharomyces genome sequence helped launch the post-genomic era, focusing on functional analyses of whole genomes. Yeast paved the way for the systematic analysis of large and complex genomes by serving as a test bed for novel experimental approaches and technologies, tools that are fast becoming the standard in drug discovery research

The role of glutamine 114 in old yellow enzyme

Glutamine 114 of OYE1 is a well conserved residue in the active site of the Old Yellow Enzyme family. It forms hydrogen bonds to the O2 and N3 of the flavoprotein prosthetic group, FMN. Glutamine 114 was mutated to asparagine, introducing an R-group that is one methylene group shorter. The resultant enzyme was characterized to determine the effect of the mutation on the mechanistic behavior of the enzyme, and the crystal structure was solved to determine the effect of the mutation on the structure of the protein. The Q114N mutation results in little change in the protein structure, moving the amide group of residue 114 out of H-bonding distance, allowing repositioning of the FMN prosthetic group to form new interactions that replace the lost H-bonds. The mutation decreases the ability to bind ligands, as all dissociation constants for substituted phenols are larger than for the wild type enzyme. The rate constant for the reductive half-reaction with beta-NADPH is slightly greater, whereas that for the oxidative half-reaction with 2-cyclohexenone is smaller than for the wild type enzyme. Oxidation with molecular oxygen is biphasic and involves formation and reaction with O(2), a phenomenon that is more pronounced with this mutation than with wild type enzyme. When superoxide dismutase is added to the reaction, we observe a single-phase reaction typical of the wild type enzyme. Turnover reactions using beta-NADPH with 2-cyclohexenone and molecular oxygen were studied to further characterize the mutant enzyme.

Saccharomyces Genome Database (SGD) provides secondary gene annotation using the Gene Ontology (GO)

The Saccharomyces Genome Database (SGD) resources, ranging from genetic and physical maps to genome-wide analysis tools, reflect the scientific progress in identifying genes and their functions over the last decade. As emphasis shifts from identification of the genes to identification of the role of their gene products in the cell, SGD seeks to provide its users with annotations that will allow relationships to be made between gene products, both within Saccharomyces cerevisiae and across species. To this end, SGD is annotating genes to the Gene Ontology (GO), a structured representation of biological knowledge that can be shared across species. The GO consists of three separate ontologies describing molecular function, biological process and cellular component. The goal is to use published information to associate each characterized S.cerevisiae gene product with one or more GO terms from each of the three ontologies. To be useful, this must be done in a manner that allows accurate associations based on experimental evidence, modifications to GO when necessary, and careful documentation of the annotations through evidence codes for given citations. Reaching this goal is an ongoing process at SGD. For information on the current progress of GO annotations at SGD and other participating databases, as well as a description of each of the three ontologies, please visit the GO Consortium page at http://www.geneontology.org. SGD gene associations to GO can be found by visiting our site at http://genome-www.stanford.edu/Saccharomyces/.

DIAN: a novel algorithm for genome ontological classification

Faced with the determination of many completely sequenced genomes, computational biology is now faced with the challenge of interpreting the significance of these data sets. A multiplicity of data-related problems impedes this goal: Biological annotations associated with raw data are often not normalized, and the data themselves are often poorly interrelated and their interpretation unclear. All of these problems make interpretation of genomic databases increasingly difficult. With the current explosion of sequences now available from the human genome as well as from model organisms, the importance of sorting this vast amount of conceptually unstructured source data into a limited universe of genes, proteins, functions, structures, and pathways has become a bottleneck for the field. To address this problem, we have developed a method of interrelating data sources by applying a novel method of associating biological objects to ontologies. We have developed an intelligent knowledge-based algorithm, to support biological knowledge mapping, and, in particular, to facilitate the interpretation of genomic data. In this respect, the method makes it possible to inventory genomes by collapsing multiple types of annotations and normalizing them to various ontologies. By relying on a conceptual view of the genome, researchers can now easily navigate the human genome in a biologically intuitive, scientifically accurate manner.

Gene expression microarrays and the integration of biological knowledge

Large-scale parallel measurement of whole-genome RNA expression is now possible with high-density arrays of cDNA or oligonucleotides. Using this technology efficiently will require the integration of other sources of biological information, such as gene identity, biomedical literature and biochemical pathway for a given gene. Such integration is essential to understand the cellular program of gene expression and the molecular physiology of an organism. Advances in microarray technology, and the expected rapid rise in microarray data will lead to new insight into fundamental biological problems such as the prediction of gene function from expression profiles and the identification of potential drug targets from biologically active compounds.

Survey of human mitochondrial diseases using new genomic/proteomic tools

BACKGROUND

We have constructed Bayesian prior-based, amino-acid sequence profiles for the complete yeast mitochondrial proteome and used them to develop methods for identifying and characterizing the context of protein mutations that give rise to human mitochondrial diseases. (Bayesian priors are conditional probabilities that allow the estimation of the likelihood of an event - such as an amino-acid substitution - on the basis of prior occurrences of similar events.) Because these profiles can assemble sets of taxonomically very diverse homologs, they enable identification of the structurally and/or functionally most critical sites in the proteins on the basis of the degree of sequence conservation. These profiles can also find distant homologs with determined three-dimensional structures that aid in the interpretation of effects of missense mutations.

RESULTS

This survey reports such an analysis for 15 missense mutations, one insertion and three deletions involved in Leber's hereditary optic neuropathy, Leigh syndrome, mitochondrial neurogastrointestinal encephalomyopathy, Mohr-Tranebjaerg syndrome, iron-storage disorders related to Friedreich's ataxia, and hereditary spastic paraplegia. We present structural correlations for seven of the mutations.

CONCLUSIONS

Of the 19 mutations analyzed, 14 involved changes in very highly conserved parts of the affected proteins. Five out of seven structural correlations provided reasonable explanations for the malfunctions. As additional genetic and structural data become available, this methodology can be extended. It has the potential for assisting in identifying new disease-related genes. Furthermore, profiles with structural homologs can generate mechanistic hypotheses concerning the underlying biochemical processes - and why they break down as a result of the mutations.

The cohesin complex: sequence homologies, interaction networks and shared motifs

BACKGROUND

Cohesin is a macromolecular complex that links sister chromatids together at the metaphase plate during mitosis. The links are formed during DNA replication and destroyed during the metaphase-to-anaphase transition. In budding yeast, the 14S cohesin complex comprises at least two classes of SMC (structural maintenance of chromosomes) proteins - Smc1 and Smc3 - and two SCC (sister-chromatid cohesion) proteins - Scc1 and Scc3. The exact function of these proteins is unknown.

RESULTS

Searches of protein sequence databases have revealed new homologs of cohesin proteins. In mouse, Mmip1 (Mad member interacting protein 1) and Smc3 share 99% sequence identity and are products of the same gene. A phylogenetic tree of SMC homologs reveals five families: Smc1, Smc2, Smc3, Smc4 and an ancestral family that includes the sequences from the Archaea and Eubacteria. This ancestral family also includes sequences from eukaryotes. A cohesion interaction network, comprising 17 proteins, has been constructed using two proteomic databases. Genes encoding six proteins in the cohesion network share a common upstream region that includes the MluI cell-cycle box (MCB) element. Pairs of the proteins in this network share common sequence motifs that could represent common structural features such as binding sites. Scc2 shares a motif with Chk1 (kinase checkpoint protein), that comprises part of the serine/threonine protein kinase motif, including the active-site residue.

CONCLUSIONS

We have combined genomic and proteomic data into a comprehensive network of information to reach a better understanding of the function of the cohesin complex. We have identified new SMC homologs, created a new SMC phylogeny and identified shared DNA and protein motifs. The potential for Scc2 to function as a kinase - a hypothesis that needs to be verified experimentally - could provide further evidence for the regulation of sister-chromatid cohesion by phosphorylation mechanisms, which are currently poorly understood.

Purification and kinetic characterization of CTP:phosphocholine cytidylyltransferase from Saccharomyces cerevisiae

CTP:phosphocholine cytidylyltransferase (CCT) regulates the biosynthesis of phosphatidylcholine in mammalian cells. In order to understand the mechanism by which this enzyme controls phosphatidylcholine synthesis, we have initiated studies of CCT from the model genetic system, the yeast Saccharomyces cerevisiae. The yeast CCT gene was isolated from genomic DNA using the polymerase chain reaction and was found to encode tyrosine at position 192 instead of histidine, as originally reported. Levels of expression of yeast CCT activity in Escherichia coli or in the yeast, Pichia pastoris, were somewhat low. Expression of yeast CCT in a baculovirus system as a 6x-His-tag fusion protein was higher and was used to purify yeast CCT by a procedure that included delipidation. Kinetic characterization revealed that yeast CCT was activated approximately 20-fold by 20 microM phosphatidylcholine:oleate vesicles, a level 5-fold lower than that necessary for maximal activation of rat CCT. The k(cat) value was 31.3 s(-1) in the presence of lipid and 1.5 s(-1) in the absence of lipid. The K(m) values for the substrates CTP and phosphocholine did not change significantly upon activation by lipids; K(m) values in the presence of lipid were 0.80 mM for phosphocholine and 1.4 mM for CTP while K(m) values in the absence of lipid were 1.2 mM for phosphocholine and 0.8 mM for CTP. Activation of yeast CCT, therefore, appears to be due to an increase in the k(cat) value upon lipid binding.

Saccharomyces Genome Database provides tools to survey gene expression and functional analysis data

Upon the completion of the SACCHAROMYCES: cerevisiae genomic sequence in 1996 [Goffeau,A. et al. (1997) NATURE:, 387, 5], several creative and ambitious projects have been initiated to explore the functions of gene products or gene expression on a genome-wide scale. To help researchers take advantage of these projects, the SACCHAROMYCES: Genome Database (SGD) has created two new tools, Function Junction and Expression Connection. Together, the tools form a central resource for querying multiple large-scale analysis projects for data about individual genes. Function Junction provides information from diverse projects that shed light on the role a gene product plays in the cell, while Expression Connection delivers information produced by the ever-increasing number of microarray projects. WWW access to SGD is available at genome-www.stanford. edu/Saccharomyces/.

Differences between predicted and observed sequences in Saccharomyces cerevisiae

We recently studied the protein composition of a Saccharomyces cerevisiae wine yeast strain (K310) of enological interest. About 2,500 spots of 8-250 kDa observed molecular mass were resolved by two-dimensional gel electrophoresis. Experimental molecular masses and isoelectric points were calculated for most of them. Twenty-seven proteins were subjected to Edman microsequencing. N-terminal sequences of 12/27 proteins were determined, whereas internal sequences of 6/27 proteins were obtained following in situ proteolysis. Comparison between the experimental data and those reported in the SWISS-PROT database revealed some differences between genotypic and phenotypic sequences. These are indicative of the changes a protein can undergo with respect to the primary structure coded by the genomic DNA. Our results highlight the need to complement genomic analysis with detailed proteomics in order to refine the vast amount of information provided by DNA sequencing and to find an exact correlation between genome and proteome.

Gene expression analysis by massively parallel signature sequencing (MPSS) on microbead arrays

We describe a novel sequencing approach that combines non-gel-based signature sequencing with in vitro cloning of millions of templates on separate 5 microm diameter microbeads. After constructing a microbead library of DNA templates by in vitro cloning, we assembled a planar array of a million template-containing microbeads in a flow cell at a density greater than 3x10(6) microbeads/cm2. Sequences of the free ends of the cloned templates on each microbead were then simultaneously analyzed using a fluorescence-based signature sequencing method that does not require DNA fragment separation. Signature sequences of 16-20 bases were obtained by repeated cycles of enzymatic cleavage with a type IIs restriction endonuclease, adaptor ligation, and sequence interrogation by encoded hybridization probes. The approach was validated by sequencing over 269,000 signatures from two cDNA libraries constructed from a fully sequenced strain of Saccharomyces cerevisiae, and by measuring gene expression levels in the human cell line THP-1. The approach provides an unprecedented depth of analysis permitting application of powerful statistical techniques for discovery of functional relationships among genes, whether known or unknown beforehand, or whether expressed at high or very low levels.

Gene ontology: tool for the unification of biology. The Gene Ontology Consortium

Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.

Gene ontology: tool for the unification of biology. The Gene Ontology Consortium

Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.

Test of intron predictions reveals novel splice sites, alternatively spliced mRNAs and new introns in meiotically regulated genes of yeast

Correct identification of all introns is necessary to discern the protein-coding potential of a eukaryotic genome. The existence of most of the spliceosomal introns predicted in the genome of Saccharomyces cerevisiae remains unsupported by molecular evidence. We tested the intron predictions for 87 introns predicted to be present in non-ribosomal protein genes, more than a third of all known or suspected introns in the yeast genome. Evidence supporting 61 of these predictions was obtained, 20 predicted intron sequences were not spliced and six predictions identified an intron-containing region but failed to specify the correct splice sites, yielding a successful prediction rate of <80%. Alternative splicing has not been previously described for this organism, and we identified two genes (YKL186C/ MTR2 and YML034W) which encode alternatively spliced mRNAs; YKL186C/ MTR2 produces at least five different spliced mRNAs. One gene (YGR225W/ SPO70 ) has an intron whose removal is activated during meiosis under control of the MER1 gene. We found eight new introns, suggesting that numerous introns still remain to be discovered. The results show that correct prediction of introns remains a significant barrier to understanding the structure, function and coding capacity of eukaryotic genomes, even in a supposedly simple system like yeast.

The Saccharomyces cerevisiae Rheb G-protein is involved in regulating canavanine resistance and arginine uptake

The new member of the Ras superfamily of G-proteins, Rheb, has been identified in rat and human, but its function has not been defined. We report here the identification of Rheb homologues in the budding yeast Saccharomyces cerevisiae (ScRheb) as well as in Schizosaccharomyces pombe, Drosophila melanogaster, zebrafish, and Ciona intestinalis. These proteins define a new class of G-proteins based on 1) their overall sequence similarity, 2) high conservation of their effector domain sequence, 3) presence of a unique arginine in their G1 box, and 4) presence of a conserved CAAX farnesylation motif. Characterization of an S. cerevisiae strain deficient in ScRheb showed that it is hypersensitive to growth inhibitory effects of canavanine and thialysine, which are analogues of arginine and lysine, respectively. Accordingly, the uptake of arginine and lysine was increased in the ScRheb-deficient strain. This increased arginine uptake requires the arginine-specific permease Can1p. The function of ScRheb is dependent on having an intact effector domain since mutations in the effector domain of ScRheb are incapable of complementing canavanine hypersensitivity of scrheb disruptant cells. Furthermore, the conserved arginine in the G1 box plays a role in the activity of ScRheb, as a mutation of this arginine to glycine significantly reduced the ability of ScRheb to complement canavanine hypersensitivity of ScRheb-deficient yeast. Finally, a mutation in the C-terminal CAAX farnesylation motif resulted in a loss of ScRheb function. This result, in combination with our finding that ScRheb is farnesylated, suggests that farnesylation plays a key role in ScRheb function. Our findings assign the regulation of arginine and lysine uptake as the first physiological function for this new farnesylated Ras superfamily G-protein.

The effect of nucleotide bias upon the composition and prediction of transmembrane helices

Transmembrane helices are the most readily predictable secondary structure components of proteins. They can be predicted to a high degree of accuracy in a variety of ways. Many of these methods compare new sequence data with the sequence characteristics of known transmembrane domains. However, the known transmembrane sequences are not necessarily representative of a particular organism. We attempt to demonstrate that parameters optimized for the known transmembrane domains are far from optimal when predicting transmembrane regions in a given genome. In particular, we have tested the effect of nucleotide bias upon the composition and hence the prediction characteristics of transmembrane helices. Our analysis shows that nucleotide bias of a genome has a strong and predictable influence upon the occurrences of several of the most important hydrophobic amino acids found within transmembrane helices. Thus, we show that nucleotide bias should be taken into account when determining putative transmembrane domains from sequence data.

Toward a protein-protein interaction map of the budding yeast: A comprehensive system to examine two-hybrid interactions in all possible combinations between the yeast proteins

Protein-protein interactions play pivotal roles in various aspects of the structural and functional organization of the cell, and their complete description is indispensable to thorough understanding of the cell. As an approach toward this goal, here we report a comprehensive system to examine two-hybrid interactions in all of the possible combinations between proteins of Saccharomyces cerevisiae. We cloned all of the yeast ORFs individually as a DNA-binding domain fusion ("bait") in a MATa strain and as an activation domain fusion ("prey") in a MATalpha strain, and subsequently divided them into pools, each containing 96 clones. These bait and prey clone pools were systematically mated with each other, and the transformants were subjected to strict selection for the activation of three reporter genes followed by sequence tagging. Our initial examination of approximately 4 x 10(6) different combinations, constituting approximately 10% of the total to be tested, has revealed 183 independent two-hybrid interactions, more than half of which are entirely novel. Notably, the obtained binary data allow us to extract more complex interaction networks, including the one that may explain a currently unsolved mechanism for the connection between distinct steps of vesicular transport. The approach described here thus will provide many leads for integration of various cellular functions and serve as a major driving force in the completion of the protein-protein interaction map.

MODBASE, a database of annotated comparative protein structure models

MODBASE is a queryable database of annotated comparative protein structure models. The models are derived by MODPIPE, an automated modeling pipeline relying on the programs PSI-BLAST and MODELLER. The database currently contains 3D models for substantial portions of approximately 17 000 proteins from 10 complete genomes, including those of Caenorhabditis elegans, Saccharomyces cerevisiae and Escherichia coli, as well as all the available sequences from Arabidopsis thaliana and Homo sapiens. The database also includes fold assignments and alignments on which the models were based. In addition, special care is taken to assess the quality of the models. ModBase is accessible through a web interface at http://guitar.rockefeller.edu/modbase/

Integrating functional genomic information into the Saccharomyces genome database

The Saccharomyces Genome Database (SGD) stores and organizes information about the nearly 6200 genes in the yeast genome. The information is organized around the 'locus page' and directs users to the detailed information they seek. SGD is endeavoring to integrate the existing information about yeast genes with the large volume of data generated by functional analyses that are beginning to appear in the literature and on web sites. New features will include searches of systematic analyses and Gene Summary Paragraphs that succinctly review the literature for each gene. In addition to current information, such as gene product and phenotype descriptions, the new locus page will also describe a gene product's cellular process, function and localization using a controlled vocabulary developed in collaboration with two other model organism databases. We describe these developments in SGD through the newly reorganized locus page. The SGD is accessible via the WWW at http://genome-www.stanford.edu/Saccharomyces/

XREFdb: cross-referencing the genetics and genes of mammals and model organisms

XREFdb supports the investigation of protein function in the context of information available through work in multiple organisms. In addition to facilitating the association of functional data among known genes from multiple organisms, XREFdb has developed strategies that provide access to information associated with as-yet unstudied genes. The database organizes protein similarity and genetic map positional information from diverse sources in the public domain to facilitate investigator evaluation of potential functional significance. XREFdb is found at URL www.ncbi.nlm.nih.gov/XREFdb

TRIPLES: a database of gene function in Saccharomyces cerevisiae

Using a novel multipurpose mini-transposon, we have generated a collection of defined mutant alleles for the analysis of disruption phenotypes, protein localization, and gene expression in Saccharomyces cerevisiae. To catalog this unique data set, we have developed TRIPLES, a Web-accessible database of TRansposon-Insertion Phenotypes, Localization and Expression in Saccharomyces. Encompassing over 250 000 data points, TRIPLES provides convenient access to information from nearly 7800 transposon-mutagenized yeast strains; within TRIPLES, complete data reports of each strain may be viewed in table format, or if desired, downloaded as tab-delimited text files. Each report contains external links to corresponding entries within the Saccharomyces Genome Database and International Nucleic Acid Sequence Data Library (GenBank). Unlike other yeast databases, TRIPLES also provides on-line order forms linked to each clone report; users may immediately request any desired strain free-of-charge by submitting a completed form. In addition to presenting a wealth of information for over 2300 open reading frames, TRIPLES constitutes an important medium for the distribution of useful reagents throughout the yeast scientific community. Maintained by the Yale Genome Analysis Center, TRIPLES may be accessed at http://ycmi.med.yale.edu/ygac/triples.htm

MitBASE : a comprehensive and integrated mitochondrial DNA database. The present status

MitBASE is an integrated and comprehensive database of mitochondrial DNA data which collects, under a single interface, databases for Plant, Vertebrate, Invertebrate, Human, Protist and Fungal mtDNA and a Pilot database on nuclear genes involved in mitochondrial biogenesis in Saccharomyces cerevisiae. MitBASE reports all available information from different organisms and from intraspecies variants and mutants. Data have been drawn from the primary databases and from the literature; value adding information has been structured, e.g., editing information on protist mtDNA genomes, pathological information for human mtDNA variants, etc. The different databases, some of which are structured using commercial packages (Microsoft Access, File Maker Pro) while others use a flat-file format, have been integrated under ORACLE. Ad hoc retrieval systems have been devised for some of the above listed databases keeping into account their peculiarities. The database is resident at the EBI and is available at the following site: http://www3.ebi.ac.uk/Research/Mitbase/mitbas e.pl. The impact of this project is intended for both basic and applied research. The study of mitochondrial genetic diseases and mitochondrial DNA intraspecies diversity are key topics in several biotechnological fields. The database has been funded within the EU Biotechnology programme.

GXD: a Gene Expression Database for the laboratory mouse: current status and recent enhancements. The Gene Expresison Database group

The Gene Expression Database (GXD) is a community resource of gene expression information for the laboratory mouse. The database is designed as an open-ended system that can integrate different types of expression data. New expression data are made available on a daily basis. Thus, GXD provides increasingly complete information about what transcripts and proteins are produced by what genes; where, when and in what amounts these gene products are expressed; and how their expression varies in different mouse strains and mutants. GXD is integrated with the Mouse Genome Database (MGD). Continuously refined interconnections with sequence databases and with databases from other species place the gene expression information in the larger biological and analytical context. GXD is accessible through the Mouse Genome Informatics Web site at http://www.informatics.jax.org/ or directly at http://www.informatics.jax.org/menus/expression_menu.shtm l

From global expression data to gene networks

Allowing the parallel monitoring of the transcription of thousands of genes, microarrays constitute a powerful technique for functional genomics. In a recent paper, a clustering method and a local alignment software were combined to identify DNA motifs in sets of yeast genes endowed with similar transcription profiles throughout mitosis (1). Identifying various known transcriptional binding sites together with new putative ones, the authors made a significant step towards a systematic characterization of the regulatory structure of genomic networks. BioEssays 1999;21:895-899.

Metabolism and selected functions of sphingolipids in the yeast Saccharomyces cerevisiae

Our knowledge of sphingolipid metabolism and function in Saccharomyces cerevisiae is growing rapidly. Here we discuss the current status of sphingolipid metabolism including recent evidence suggesting that exogenous sphingoid long-chain bases must first be phosphorylated and then dephosphorylated before incorporation into ceramide. Phenotypes of strains defective in sphingolipid metabolism are discussed because they provide hints about the undiscovered functions of sphingolipids and are one of the major reasons for studying this model eukaryote. The long-chain base phosphates, dihydrosphingosine-1-phosphate and phytosphingosine-1-phosphate, have been hypothesized to play roles in heat stress resistance, perhaps acting as signaling molecules. We evaluate the data supporting this hypothesis and suggest future experiments needed to verify it. Finally, we discuss recent clues that may help to reveal how sphingolipid synthesis and total cellular sphingolipid content are regulated.

Novel approaches for discovering industrial enzymes

New technologies for enzyme discovery are changing the rules of the game for industrial biocatalysis. More kinds of enzymes are available, their hardiness is increasing, and their costs are coming down. These changes are the key drivers for a rebirth of interest in industrial applications of enzymes. The major enabling discovery approaches include screening of biodiversity, genomic sequencing, directed evolution and phage display.